深度学习开源书进阶EN6–12 周★ 20k56 章可站内阅读
Deep Learning with Python Notebooks
《Python 深度学习》配套 Jupyter 笔记本
François Chollet· 20,230 stars
François Chollet《Deep Learning with Python》配套 Jupyter 笔记本，覆盖数学积木、卷积网络、Transformer 与生成模型。适合对照教材动手复现的深度学习学习者。
为什么收录 · 补齐 Keras 作者教材配套 notebook，丰富 foundations 层架经典书目。
Keras深度学习教材计算机视觉NLP
这本书强在哪
- GitHub 高星真学习内容
- 可导入站内阅读
- 材料结构清晰
- 适合系统跟学
建议怎么学
- 01按大纲顺序推进
- 02每章留下自己的实验记录
- 03卡点时回看对应知识库文章
适合谁 / 前置
- Python 基础
- 愿意跟练代码或笔记
学完得到什么
- 掌握该教程主线知识点
- 能复现关键实验或练习
- 建立可对照的学习笔记
- 为下一阶段课程打底
目录
来自站内阅读器镜像；点击章节直接阅读
first edition19 章
- 17A first look at a neural networknotebook
- 18Classifying movie reviews: a binary classification examplenotebook
- 19Classifying newswires: a multi-class classification examplenotebook
- 20Predicting house prices: a regression examplenotebook
- 21Overfitting and underfittingnotebook
- 225.1 - Introduction to convnetsnotebook
- 235.2 - Using convnets with small datasetsnotebook
- 24Using a pre-trained convnetnotebook
- 25Visualizing what convnets learnnotebook
- 26One-hot encoding of words or charactersnotebook
- 27Using word embeddingsnotebook
- 28Understanding recurrent neural networksnotebook
- 29Advanced usage of recurrent neural networksnotebook
- 30Sequence processing with convnetsnotebook
- 31Text generation with LSTMnotebook
- 32Deep Dreamnotebook
- 33Neural style transfernotebook
- 34Generating imagesnotebook
- 35Introduction to generative adversarial networksnotebook
Notebook16 章
- 01The mathematical building blocks of neural networksnotebook
- 02Introduction to TensorFlow, PyTorch, JAX, and Kerasnotebook
- 03Classification and regressionnotebook
- 04Fundamentals of machine learningnotebook
- 05A deep dive on Kerasnotebook
- 06Image classificationnotebook
- 07ConvNet architecture patternsnotebook
- 08Interpreting what ConvNets learnnotebook
- 09Image segmentationnotebook
- 10Object detectionnotebook
- 11Timeseries forecastingnotebook
- 12Text classificationnotebook
- 13Language models and the Transformernotebook
- 14Text generationnotebook
- 15Image generationnotebook
- 16Best practices for the real worldnotebook
second edition21 章
- 36The mathematical building blocks of neural networksnotebook
- 37Introduction to Keras and TensorFlownotebook
- 38Getting started with neural networks: Classification and regressionnotebook
- 39Fundamentals of machine learningnotebook
- 40Working with Keras: A deep divenotebook
- 41Introduction to deep learning for computer visionnotebook
- 42Advanced deep learning for computer visionnotebook
- 43Modern convnet architecture patternsnotebook
- 44Interpreting what convnets learnnotebook
- 45Deep learning for timeseriesnotebook
- 46Deep learning for textnotebook
- 47chapter11 part02 sequence modelsnotebook
- 48The Transformer architecturenotebook
- 49Beyond text classification: Sequence-to-sequence learningnotebook
- 50Generative deep learningnotebook
- 51DeepDreamnotebook
- 52Neural style transfernotebook
- 53Generating images with variational autoencodersnotebook
- 54Introduction to generative adversarial networksnotebook
- 55Best practices for the real worldnotebook
- 56Conclusionsnotebook
策展亮点章节
Math Building BlocksML FrameworksClassificationConvNetsTransformersGeneration
仓库数据
Stars20,230
Forks9,058
主要语言Jupyter Notebook
创建2017/9/6
更新2026/8/11
fbf7f1bf（master）· 许可证 MIT License (MIT)。 上游更新后可通过同步脚本刷新。